A review of Mendelian randomization in amyotrophic lateral sclerosis

Abstract

Abstract Amyotrophic lateral sclerosis (ALS) is a relatively common and rapidly progressive neurodegenerative disease which, in the majority of cases, is thought to be determined by a complex gene-environment interaction. Exponential growth in the number of performed genome-wide association studies (GWAS), combined with the advent of Mendelian randomization (MR) is opening significant new opportunities to identify environmental exposures which increase or decrease the risk of ALS. Each of these discoveries has the potential to shape new therapeutic interventions. However, to do so rigorous methodological standards must be applied in the performance of MR. We have performed a review of MR studies performed in ALS to date. We identified 20 MR studies, including evaluation of physical exercise, adiposity, cognitive performance, immune function, blood lipids, sleep behaviours, educational attainment, alcohol consumption, smoking and type 2 diabetes mellitus. We have evaluated each study using gold standard methodology supported by the MR literature and the STROBE-MR checklist. Where discrepancies exist between MR studies, we suggest the underlying reasons. A number of studies conclude that there is a causal link between blood lipids and risk of ALS; replication across different datasets and even different populations adds confidence. For other putative risk factors, such as smoking and immune function, MR studies have provided cause for doubt. We highlight the use of positive control analyses in choosing exposure SNPs to make up the MR instrument, use of SNP clumping to avoid false positive results due to SNPs in linkage, and the importance of multiple testing correction. We discuss the implications of survival bias for study of late age of onset diseases such as ALS, and make recommendations to mitigate this potentially important confounder. For MR to be useful to the ALS field, high methodological standards must be applied to ensure reproducibility. MR is already an impactful tool but poor quality studies will lead to incorrect interpretations by a field which includes non-statisticians, wasted resources and missed opportunities.